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iHelp: Techniques for early risk identification, predictions and assessment I

Authors: Thanos Kalligeris; Giorgos Giotis; Maritini Kalogerini; Spyros Papafragkos; Anastasios Pantazidis; Aristodemos Pnevmatikakis; Krasimir Filipov;

iHelp: Techniques for early risk identification, predictions and assessment I

Abstract

This report summarizes the actions performed under Task 5.1 - “Techniques for early risk identification, predictions and assessment I” and more specifically provides details of the Artificial Intelligence (AI) / Machine Learning (ML) algorithms and implementation techniques that will be used to perform early identification as well as predictions of Pancreatic Cancer (PC) risks in individuals. The deliverable brings together several AI/ML techniques that are expected to be able to identify hidden patterns and trends in given data. When the data become available in the next phase of the project, these techniques will be used for performing assessment of identified risks and subsequently for making predictions. Since this is the first version of a series of deliverables, the follow-up report (i.e. D5.2) will specify the techniques to be used and linked with the datasets collected from the project pilots.

Keywords

pancreatic cancer, iHelp, early risk assessment, Techniques for early risk identification, predictions and assessment, early risk identification, early risk predictions

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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